Jayant Shekar

Jayant Shekar is Solutions Architect at Cloudera working with various large and small companies in various verticals on their big data use cases, architecture, algorithms and deployments. Prior to Cloudera Jayant also worked at Yahoo where he was instrumental in building out the large scale content/listings platform using Hadoop and big data technologies and working with various Yahoo properties, real estate, autos, local, news, movies etc. Prior to Yahoo, Jayant worked at eBay building out a new shopping platform (K2) using Nutch/Hadoop, search intelligence platform, among others. Jayant also worked at KLA-Tencor building software for Reticle Inspection Stations and defect analysis systems. Jayant has bachelor’s degree in computer science from IIT Kharagpur and a master’s degree in computer engineering from San Jose State University.

Building recommendation platforms with Hadoop

Preview of upcoming session at the Strata Conference

Recommendations are making their way into more and more products. Using larger datasets are significantly improving the recommendations. Hadoop is being increasingly used for building out the recommendation platforms. Some of the examples of Recommendations include product recommendations, merchant recommendations, content recommendations, social recommendations, query recommendation, display and search ads.

With the number of options available to the users ever increasing, the attention span of customers is getting lower and lower at the very fast pace. At any given moment, the customers are getting used to seeing their best choices right in front of them. In such a scenario, we see recommendations powering more and more features of the products and driving user interaction. Hence companies are looking for more ways to minutely target customers at the right time. This brings in big data into the picture. Succeeding with data and building new markets, or changing the existing markets is the game being played in many high stake scenarios. Some companies have found the way to build their big data recommendation/machine learning platform giving them the edge in bringing better and better products ever faster to the market. Hence, there is a strong case for looking at recommendations/machine learning on big data as a platform in a company, rather than something of a black box that magically produces the right results. The platform allows us to build various other features like fraud detection, spam detection, content enrichment and serving etc. making it viable in the long run. It is not just about recommendations.

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